Boosting Transition Matrix Method with Implicit Regularization: Handling Diverse Label Noise

This paper proposes an enhanced Transition Matrix Method incorporating implicit regularization for robustly handling diverse label noise in machine learning models. The method effectively addresses challenges posed by noisy datasets, leading to improved model accuracy and reliability. Implicit regularization, a powerful technique, is integrated into the Transition Matrix Method to enhance its resilience against noise. The proposed approach not only mitigates the negative impacts of noisy labels but also leverages the inherent structure of the data to improve model performance. Experimental results demonstrate the superiority of the proposed method in handling diverse label noise compared to existing techniques. The findings highlight the significance of implicit regularization in enhancing the robustness of machine learning models in the presence of noisy data.